Energy Efficient Computing through Productivity-Aware Frequency Scaling

This paper proposes a new policy for dynamic frequency scaling: productivity-aware frequency scaling (PAFS). PAFS aims at optimizing energy consumptions while still satisfying performance requirements of a given application. In contrast to the commonly-used on demand frequency scaling, PAFS may keep the processor in a power save state even in high CPU-usage situations. This will be the case as long as the application (or set of applications) for which productivity is to be preserved presents acceptable performance (e.g., as stablished by a QoS contract). Our experiments show savings of up to 23.65% in energy consumption when compared to the commonly used on demand DFS policy with no performance degradation for the productivity metric. PAFS is, therefore, binded to a single or a set of applications running in a machine. Nevertheless, compared to previous approaches to application-specific frequency scaling, PAFS does not require modifying the application or a calibration process. PAFS requires only a productivity metric which may already be exported by an application (e.g., through a log file, such as response time or throughput in an Apache web server) or which may be computed through a simple program or script.